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run_rolling_xi_acf,
run_rolling_xi_ccf) to strictly comply with CRAN server
policies (e.g., respecting MC_CORES and
OMP_THREAD_LIMIT). Replaced
parallel::detectCores() with
max(1L, parallelly::availableCores() - 1L) to ensure safe
execution without exceeding permitted limits or causing 0-core
crashes.future::plan(sequential)) via
setup.R across the entire testthat suite to
adhere to CRAN’s 2-core testing limit.xi_matrix() where variable names
(var_names) were inadvertently omitted from the output
object, resulting in empty variable lists during
print().xi_ccf()
to utilize the centralized internal helper
check_surrogate_count()."Error:" and
"Warning:" prefixes in custom message strings across the
package to ensure native R console formatting.sig_level validation in xi_matrix().max_iter argument in
run_rolling_xi_acf() and run_rolling_xi_ccf(),
allowing users to explicitly tune the maximum number of iterations for
the IAAFT/MIAAFT surrogate convergence.xi_acf().xi_ccf and xi_matrix. The Max-Statistic null
distribution is now strictly evaluated independently for Contemporaneous
(Lag 0) and Temporal (Lag > 0) dependencies. This resolves the “Lag-0
Masking Effect,” significantly improving the statistical power to detect
delayed causal propagation.xi_matrix to prevent arbitrary threshold inflation.%dofuture% with
explicit package loading.Window_ID tracking column in the outputs
of run_rolling_xi_acf and
run_rolling_xi_ccf.check_surrogate_count) to rigorously validate
user-provided n_surr against the dynamic size of FWER test
families.xi_ccf() now
explicitly separates causal directions (direction = "both",
"x_leads", "y_leads") and returns Tidy data
frames for easier downstream EDA.xi_matrix() Max-Statistic
empirical null distribution, restoring statistical power for detecting
true cross-edge pathways.xi_matrix() C++ calculations to
synchronize behavior with xi_ccf().extract_xi_acf(), extract_xi_ccf()) to
dynamically recompute exact FWER thresholds using preserved raw data
(data_raw).autoplot() methods for xi_ccf (vertical
faceting) and xi_matrix (diagonal variable labels) to
provide publication-ready, unified Tidyverse compatibility.xi_acf, xi_ccf, xi_matrix).autoplot
methods to produce publication-ready ggplot2 charts
utilizing base R expression() for native math
rendering.sig_level = 0.95 (confidence
level) inputs to significance levels, and systematically deprecated
older functions with proper warnings.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.